Zhiyu Wang | 王智语

Research Scientist @ Sapient Intelligence
MPhil Advanced Computer Science @ Cambridge
BSc Computer Science @ UCL

My work spans geometric machine learning, large language models, and agentic systems, with a particular interest in bringing these approaches together for AI-driven biological research.

I am currently a Research Scientist at Sapient Intelligence, where I focus on developing Praxit, a general-purpose autonomous AI-scientist framework designed for long-running research tasks across domains. I am also responsible for applying the company’s Hierarchical Reasoning Model (HRM) to healthcare scenarios, including weight management.

Before joining Sapient, I was a Master’s student at Cambridge under the supervision of Prof. Pietro Liò, where I focused on AI for proteins. I first-authored PLASMA, an ICLR 2026 paper on protein substructure alignment, and received the Highly Commended M.Phil. Project Prize for my Master’s thesis on applying hierarchical hypergraphs to protein representation learning.

News

Aug 2026	I am presenting Praxit at AI4 2026 in Las Vegas—come see us at our booth!
Jan 2026	Our PLASMA paper on protein local alignment via optimal transport has been accepted at ICLR 2026!
Jun 2025	I am delighted to announce that I have been awarded a £350 Churchill College Postgraduate Academic Travel Fund (PAT0062) to attend IJCAI25 and present my GraphAU-Pain paper.
Jun 2025	Our GraphAU-Pain paper has been accepted for presentation at the MiGA workshop at IJCAI25. Looking forward to seeing everyone in Guangzhou, China on August 28th!

Selected Publications

ICLR
Fast and Interpretable Protein Substructure Alignment via Optimal Transport

Zhiyu Wang^*, Bingxin Zhou^*, Jing Wang, and 4 more authors

In International Conference on Learning Representations (ICLR), 2026

Poster; * Equal contribution

Abs Paper Bib

PLASMA is a module based on optimal transport that can be seamlessly added to any protein representation learning model with minimal training or even no training to enable fast and highly accurate identification of locally similar substructures. Extensive experiments on 7 backbone protein language models across 3 tasks from the VenusX dataset demonstrated consistent and significant improvement (+10-30% ROC-AUC) across all backbones and tasks.
@inproceedings{wang2026plasma, title = {Fast and Interpretable Protein Substructure Alignment via Optimal Transport}, author = {Wang, Zhiyu and Zhou, Bingxin and Wang, Jing and Tan, Yang and Zhao, Weishu and Li\`o, Pietro and Hong, Liang}, year = {2026}, booktitle = {International Conference on Learning Representations (ICLR)}, eprint = {2510.11752}, archiveprefix = {arXiv}, primaryclass = {q-bio.BM}, github = {ZW471/PLASMA-Protein-Local-Alignment}, note = {Poster; * Equal contribution} }
arXiv
Topotein: Topological Deep Learning for Protein Representation Learning

Zhiyu Wang, Arian R. Jamasb, Mustafa Hajij, and 3 more authors

2025

Abs Paper Bib

Topotein introduces a comprehensive framework that applies topological deep learning to protein representation learning through the novel Protein Combinatorial Complex (PCC) and Topology-Complete Perceptron Network (TCPNet). TCPNet is an SE(3)-equivariant topological neural network that simultaneously learns protein residue-level and secondary structure-level features without losing any geometric information, demonstrating consistent improvements across four protein representation learning tasks.
@article{wang2025tcpnet, title = {Topotein: Topological Deep Learning for Protein Representation Learning}, author = {Wang, Zhiyu and Jamasb, Arian R. and Hajij, Mustafa and Morehead, Alex and Braithwaite, Luke and Li\`o, Pietro}, year = {2025}, eprint = {2509.03885}, archiveprefix = {arXiv}, primaryclass = {cs.LG}, github = {ZW471/TopoteinWorkshop} }